Evidence map›Paper›PMID 29038668›Full record

ArticleJournal of clinical medicine research2017

Overcrowding and Its Association With Patient Outcomes in a Median-Low Volume Emergency Department.

J Laureano Phillips, Bradford E Jackson, Elizabeth L Fagan, Steven E Arze, Brenton Major, Nestor R Zenarosa, Hao Wang

Abstract read
In one paragraph

Article in Journal of clinical medicine research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. A pre-operative gastrointestinal optimization protocol to improve outcomes after intrathecal baclofen pump surgery.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025
    Article
  8. Article
  9. Review
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  11. Article
  12. Observational
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  14. Article
  15. Outcomes of Crowding in Emergency Departments; a Systematic Review.Archives of academic emergency medicine · 2019
    Review
  16. Time series model for forecasting the number of new admission inpatients.BMC medical informatics and decision making · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

J Laureano PhillipsOffice of Clinical Research, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA.
Bradford E JacksonCenter for Outcomes Research, John Peter Smith Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA.
Elizabeth L FaganDepartment of Emergency Medicine, Baylor Scott & White Medical Center at McKinney, 5252 W. University Dr., McKinney, TX 75071, USA.
Steven E ArzeIntegrative Emergency Services, 13737 Noel Rd., Suite 1200, Dallas, TX 75240, USA.
Brenton MajorDepartment of Emergency Medicine, Baylor Scott & White Medical Center at McKinney, 5252 W. University Dr., McKinney, TX 75071, USA.
Nestor R ZenarosaIntegrative Emergency Services, 13737 Noel Rd., Suite 1200, Dallas, TX 75240, USA.
Hao WangIntegrative Emergency Services, 13737 Noel Rd., Suite 1200, Dallas, TX 75240, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCrowding occurs commonly in high volume emergency departments (ED) and has been associated with negative patient care outcomes. We aim to assess ED crowding in a median-low volume setting and evaluate associations with patient care outcomes.

methodsThis was a prospective single-center study from November 14, 2016 until December 14, 2016. ED crowding was measured every 2 h by three different estimation tools: National Emergency Department Overcrowding Score (NEDOCS); Community Emergency Department Overcrowding Score (CEDOCS); and Severely-overcrowding Overcrowding and Not-overcrowding Estimation Tool (SONET) categorized under six different levels of crowding (not busy, busy, extremely busy, overcrowded, severely overcrowded, and dangerously overcrowded). Crowding scores were assigned to each patient upon ED arrival. We evaluated the distributions of crowding and patient ED length of stay (ED LOS) across estimation tools. Accelerated failure time models were utilized to estimate time ratios and their corresponding 95% confidence intervals comparing median LOS across levels of crowding within each estimation tool.

resultsThis study comprised 2,557 patients whose median ED LOS was 150 min. Approximately 2% of patients arrived during 2 h time intervals deemed overcrowded regardless of the crowding tool used. Median ED LOS increased with the increased level of ED crowding and prolonged median ED LOS (> 150 min) occurred at ED of extremely busy status. Time ratios ranged from 1.09 to 1.48 for NEDOCS, 1.25 - 1.56 for CEDOCS, and 1.26 - 1.72 for SONET.

conclusionOvercrowding rarely occurred in study ED with median-low annual volume and might not be a valuable marker for ED crowding report. Though similar patterns of prolonged ED LOS occurred with increased levels of ED crowding, it seems crowding alerts should be initiated during extremely busy status in this ED setting.

Indexed as

CrowdingEmergency departmentLength of stay

Identifiers

PMID29038668
PMCPMC5633091

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.